A Cascading Multi-Stage Deep Learning Approach for Detecting Chagas Disease from Electrocardiograms - Sandelin, Jonas; Orabe, Zoher; Elnaggar, Ismail; Karhinoja, Katri; Zhao, Yangyang; Patiño, Chito; Kaisti, Matti; Airola, Antti (United States, 2026)
Aims: For the PhysioNet Challenge 2025, our team ”bug busters” developed an approach to detect Chagas disease from electrocardiograms. This parasitic infection can be life-threatening when untreated, and electocardiogram based screening could direct limited resources more efficiently.
Methods: We implemented a novel multi-stage cascading approach using five deep learning models: two ResNet18 variants with attention mechanisms, two SimpleCNN models, and an AttentionCNN. Our key innovation is a progressive filtering pipeline that ranks healthy samples by their prediction scores and removes those most confidently classified as healthy, creating increasingly focused training sets.
Results: Our approach scored 0.369 in the official stage on the validation dataset and 0.224 in the test set. Our team was ranked 14th out of 41.
Conclusion: The cascading multi-stage methodology shows promise for Chagas disease detection, overcoming the limitations of single-model approaches. Future work should investigate performance across diverse patient populations and explore interpretability of model decisions.
Computing in Cardiology 2025